This invention relates to a method and means for analyzing the electroretinogram (ERG) response of the eye to a quasi-random light stimulus.
The fact that the eye provides an electrical response upon illumination is quite well known. It has been demonstrated by connecting two non-polarizable electrodes, one placed over the cornea of the eye and the other on a nearby region of the skin to a suitable detector. Attempts have been made to use this phenomenon for studying retinal functions and for diagnosing retinal diseases.
The usual technique for obtaining a corneal contact is to use a large contact lens with an electrode buried therein. This is uncomfortable for the subject, which tends to increase the noise levels resulting from blinks and eye movements.
Aside from the apparatus problem as indicated above, the retina is a non-linear biological system and a diagnosis of a non-linear system is difficult, especially in real time. In 1958, Norbert Wiener produced a general theory of non-linear system analysis and synthesis. This theory assumes only time-invariance and finite memory; and therefore is applicable to many physical and living systems. Wiener proposed that a non-linear system could be identified by its response to a Gaussian white-noise stimulus, since with such an input the system is tested effectively with all possible inputs (in practice, with a great variety of inputs depending upon the length of the experiment and bandwidth of the stimulus). Wiener's original formulation was not practical for experimental applications, but Lee and Schetzen in 1965, proposed a simpler formulation in terms of cross-correlations between the stimulus and the response. This modification of the Wiener technique provided a feasible approach to analyzing time-invariant, finite memory systems.
Wiener derived the following functional expansion to define the response y to a Gaussian white-noise input x: ##EQU1## where P is the power spectral density of the quasi-random input and by definition is a constant.
The set of kernels (h.sub.0, h.sub.1, h.sub.2, . . . ) completely characterizes the system. Each kernel h.sub.n is a symmetric function of its arguments. The kernels describe quantitatively the nonlinear cross-talk between different portions of the past of the input as it affects the system response at the present, e.g., how much the response to n different pulses deviates from the superimposed responses to single pulses.
The above-indicated formula y(t)=F[x(t)] indicates a method which is particularly well suited for the study of biological systems. In operation, a stimulus x, is applied to the system, and the output y, is measured. The Wiener formulation demands that the stimulus is a Gaussian white-noise signal which in theory contains all possible stimuli, thus the resulting characterization contains information about the system's response to nearly all stimuli. It can be shown that by multiplying ternary versions of x with values of y the h.sub.0, h.sub.1 and h.sub.2 Wiener kernels can be approximated quite closely.
In an application Ser. No. 715,703, filed Aug. 19, 1976, entitled "System for Computing Weiner Kernels" by Koblasz et al., which is assigned to a common assignee, there is described a computer for solving the indicated equations for first and secondorder kernels. Thus, with the provision of a computer which can be used for diagnosing a nonlinear system, there still remains the problems of accurately measuring the human ERG signals in a manner which is comfortable for the subject and applying these signals to the computer.